FIT is an enterprise AI development framework with a multilingual function engine, a flow orchestration engine, and Java tools for connecting language models, knowledge bases, and external tools. It is for Java-oriented teams building AI applications that can run locally or in distributed deployments with plugins. The catalogue add-ons provide commands, skills, instructions, and settings for working with FIT.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add commands/modelengine-group/fit-framework/fit-analyze-dependabotgit clone --depth 1 https://github.com/ModelEngine-Group/fit-frameworkWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/modelengine-group/fit-framework/fit-analyze-dependabot)<a href="https://agentmods.dev/commands/modelengine-group/fit-framework/fit-analyze-dependabot"><img src="https://agentmods.dev/badge/commands/modelengine-group/fit-framework/fit-analyze-dependabot.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00013 | $0.00620 |
| Opus 5 | $0.00006 | $0.00310 |
| Sonnet 5 | $0.00003 | $0.00124 |
| Haiku 4.5 | $0.00001 | $0.00062 |
Grade A, and why
fit-analyze-dependabot scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
分析 Dependabot 安全告警 #$1,评估安全风险并创建修复任务。
执行以下步骤:
-
获取安全告警信息:
gh api repos/{owner}/{repo}/dependabot/alerts/$1提取: severity, summary, package name, vulnerable version range, first patched version, GHSA/CVE ID
-
创建任务目录和文件:
date +%Y%m%d-%H%M%S mkdir -p .ai-workspace/active/TASK-<timestamp>/使用 Write 工具基于 .agents/templates/task.md 模板创建 task.md:
- security_alert_number: $1
- severity, cve_id, ghsa_id
- current_step: security-analysis
- assigned_to: codex
-
分析受影响范围:
- 搜索项目中使用该依赖的所有位置(grep pom.xml/package.json 等)
- 分析是否直接使用了漏洞代码路径
- 识别依赖关系(直接依赖 vs 传递依赖)
-
评估安全风险:
- 漏洞的实际影响(是否可被利用)
- 触发条件和场景
- 修复的紧急程度
-
输出分析文档到 analysis.md,包含:
- 告警基本信息(编号、严重程度、GHSA/CVE)
- 漏洞详情(受影响包、版本范围、修复版本)
- 影响范围评估(受影响代码和功能)
- 安全风险评估(可利用性、触发条件、影响程度)
- 技术依赖和约束
- 参考链接
-
更新任务状态:
- current_step: security-analysis
- updated_at: 当前时间
- 标记 analysis.md 为已完成
-
告知用户:
- 输出漏洞严重程度、任务ID、风险等级
- 提示下一步设计修复方案:
- Claude Code / OpenCode: /plan-task
- Gemini CLI: /fit:plan-task
- Codex CLI: /prompts:fit-plan-task
- 如果是误报，关闭告警:
- Claude Code / OpenCode: /close-dependabot $1
- Gemini CLI: /fit:close-dependabot $1
- Codex CLI: /prompts:fit-close-dependabot $1
注意事项:
- Critical/High 级别立即处理,Medium 计划处理,Low 可延后
- 专注于信息收集和风险评估,不在此阶段制定修复方案
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 5d ago First seen · 64 lines · 13 tokens per session scan A 65ce1ffe0096
fit-analyze-dependabot is a command published in the GitHub repository ModelEngine-Group/fit-framework (2,116 stars, last pushed 5mo ago), licensed MIT. It adds 13 tokens to every session and 620 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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